Method for extracting brain electrical character of imagine movement of single side podosoma

Abstract

The invention relates to an extraction method of EEG characteristics of imagination of single-side limb motion in a brain-computer interface device. The classification of the characteristics of imagination of single-side motion has stronger pertinence, which is decided by the nature of excluding same tasks and extracting different tasks of a CSP space filtering method; at the same time, due to the combination of CSP algorithm and FDA characteristics extraction, the dimension of input vectors is reduced, and the marketability of a classifier is enhanced; therefore, the classification accuracy rate is enhanced to a certain extent; adopting Fisher Differentiation and Analysis (FDA), ten-dimension input vector v1, v2(v1 is four-dimension, and v2 is six-dimension) are reduced into two one-dimension input vectors f1, f2, and then classification is carried out by a support vector machine, thereby not only improving classification accuracy rate, but also avoiding the problem of dimension disaster caused by too high dimension ,as well as being beneficial to the popularization of the classifier.

Description

technical field [0001] The invention relates to a method for extracting imaginary action potentials in a brain-computer interface (BCI) system. Background technique [0002] At present, there are many diseases that can damage the neuromuscular pathways that communicate and control the brain with the external environment, such as cerebral palsy, multiple sclerosis and amyotrophic lateral sclerosis (Amyotrophic Lateral Sclerosis, ALS). These disorders cause a person to lose some or all of their voluntary muscle control. Modern life support technology can prolong the life time of patients, but the quality of life of patients is low, and the burden on families and society is also very heavy. [0003] With the advancement of computer technology and the deepening of brain function research, people began to try to establish a new communication and control pathway that does not depend on muscles to transmit information and commands between the brain and the outside world. This is t...

AI Technical Summary

Problems solved by technology

However, due to the nonlinearity of EEG, the resulting feature vectors are hardly linearly separable, so linear classification methods inevitably lead to low recognition rates when classifying spontaneous EEG signals.

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